Keep Moving Forward

When an automated system answers instead of a person, Ryan Estis has one reflex: ask for a human, immediately. It's the same instinct he says most AI adoption plans forget to plan for. Ryan Estis, keynote speaker and co-author of Prepare for Impact, calls that instinct authentic intelligence. In this episode of Keep Moving Forward, he talks about what it actually takes to lead a team well as AI changes the shape of the work, and why coaching still depends on a person who can build trust rather than just track performance. He also gets into why the return on AI investment comes down to leadership judgment more than the technology itself. They cover: - Why the same AI tools available to everyone stopped being anyone's advantage - What Ryan means by authentic intelligence, and what automate the tangible, humanize the intangible actually rules out - Why retention still comes down to the relationship between a manager and their team, not the technology layered on top - Ryan's idea of building a "leadership family tree," and why he believes good leaders prepare people to eventually move on - How Ryan manages a mix of human employees and AI agents inside his own business

Ee Lyn Khoo runs weekly AI experiments across every function of the business. Danny Fields builds the guardrails that make that safe to do. Neither one thinks the other is wrong. Danny, CTO, and Ee Lyn, Chief People Officer at Avalara, join Keep Moving Forward to break down how they arrived at the same AI accountability principle from opposite sides of the business, and why a no-blame engineering review can quietly become a no-accountability one. They also get into what actually separates useful experimentation from reckless improvising. They cover: - Why Ee Lyn's weekly cross-functional AI experiments only work because of the guardrails Danny builds around them - Why a tool is something people opt into, but infrastructure is something everybody depends on - What changes about hiring, onboarding, and training once "good" AI use gets a shared definition - Why every AI-prepared tax return at Avalara still needs a human to click approve before it's filed - How a no-blame engineering review keeps resilience from becoming an excuse

An employee spent two years waiting for a promotion he never once asked for. His manager had no idea he wanted it. Ben Brooks, founder and CEO of PILOT, has watched that gap play out from both sides of the desk more times than he can count. In this episode of Keep Moving Forward, he breaks down why assuming someone already knows what you want almost never works, why he built his company around a list of problems instead of one big idea, and where AI coaching quietly trades accountability for approval. They cover: - Why an employee waited two years to ask for a promotion he thought he'd already earned - Why most managers assume silence means satisfaction, and most employees assume effort speaks for itself - Why fear of rejection, not laziness, is what keeps people from asking for what they want - Why Ben built PILOT around a list of problems instead of a single idea worth chasing - What separates a coach who builds real accountability from an AI tool that's built to flatter

Your AI implementation strategy is only as good as the talent you have to execute it. This episode of Keep Moving Forward is adapted from a live webinar hosted by Gemma Versace of X-Team in partnership with SmartBrief featuring Craig Stewart, senior vice president of product and engineering at Stord, Paola Jacobs, vice president of human resources at Stord, and Christine Park, chief people and AI transformation officer at Branch. They talk about why executives report far more confidence in their organization's AI talent than the people doing the day-to-day work actually feel, what AI talent really means once you get past the headlines about scarcity and costs, and how three different leaders are closing that fluency gap from their own perspectives. They also cover why organizations using embedded AI teams, rather than internal staff alone or short-term contractors, are seeing meaningfully stronger outcomes.

Many organizations manage enterprise AI adoption through watching login counts. Track usage, watch the dashboard climb, call it done. Porter Orr watches for something different: a language shift, the moment a team stops calling the tool "it" and starts giving it a name. In this episode of Keep Moving Forward, Porter explains why measuring AI success only in cost savings is a mistake, how his team built adoption in stages, and the exact language shift that tells him a tool has been fully embraced in organizational workflows. They cover: - Why focusing only on cost savings with AI is thinking too small - How The Standard built adoption in stages, starting with a low-stakes internal chatbot - The language shift that signals when a tool has become a trusted teammate - Why building governance in from day one speeds up deployment instead of slowing it down